Overview

Dataset statistics

Number of variables17
Number of observations117304
Missing cells457182
Missing cells (%)22.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Categorical6
DateTime2

Alerts

dim_comment has constant value ""Constant
id_external has a high cardinality: 117185 distinct valuesHigh cardinality
dim_name has a high cardinality: 1799 distinct valuesHigh cardinality
id_store is highly overall correlated with id_table and 1 other fieldsHigh correlation
id_table is highly overall correlated with id_store and 3 other fieldsHigh correlation
id_waiter is highly overall correlated with id_store and 3 other fieldsHigh correlation
id_customer is highly overall correlated with id_tableHigh correlation
id_device is highly overall correlated with id_table and 1 other fieldsHigh correlation
m_cached_payed is highly overall correlated with m_cached_priceHigh correlation
m_cached_price is highly overall correlated with m_cached_payedHigh correlation
dim_status is highly overall correlated with dim_sourceHigh correlation
dim_type is highly overall correlated with id_waiterHigh correlation
dim_source is highly overall correlated with id_waiter and 2 other fieldsHigh correlation
dim_status is highly imbalanced (97.0%)Imbalance
dim_type is highly imbalanced (> 99.9%)Imbalance
id_table has 95387 (81.3%) missing valuesMissing
id_waiter has 59927 (51.1%) missing valuesMissing
id_customer has 115437 (98.4%) missing valuesMissing
id_device has 8398 (7.2%) missing valuesMissing
dim_name has 60751 (51.8%) missing valuesMissing
dim_comment has 117210 (99.9%) missing valuesMissing
m_cached_payed is highly skewed (γ1 = 20.40843559)Skewed
m_cached_price is highly skewed (γ1 = 20.41067901)Skewed
id_external is uniformly distributedUniform
id_order has unique valuesUnique

Reproduction

Analysis started2023-11-08 14:28:58.754306
Analysis finished2023-11-08 14:29:21.339023
Duration22.58 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id_order
Real number (ℝ)

Distinct117304
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90489232
Minimum54304217
Maximum1.5052558 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:21.462586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum54304217
5-th percentile57018624
Q168975726
median85227102
Q31.0915759 × 108
95-th percentile1.3966136 × 108
Maximum1.5052558 × 108
Range96221365
Interquartile range (IQR)40181861

Descriptive statistics

Standard deviation25346971
Coefficient of variation (CV)0.28011035
Kurtosis-0.75114091
Mean90489232
Median Absolute Deviation (MAD)18964352
Skewness0.52363833
Sum1.0614749 × 1013
Variance6.4246893 × 1014
MonotonicityNot monotonic
2023-11-08T15:29:21.701824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55538867 1
 
< 0.1%
98394465 1
 
< 0.1%
141379937 1
 
< 0.1%
137955892 1
 
< 0.1%
81456389 1
 
< 0.1%
80582874 1
 
< 0.1%
80224543 1
 
< 0.1%
146010386 1
 
< 0.1%
137143248 1
 
< 0.1%
98452252 1
 
< 0.1%
Other values (117294) 117294
> 99.9%
ValueCountFrequency (%)
54304217 1
< 0.1%
54304720 1
< 0.1%
54305289 1
< 0.1%
54305380 1
< 0.1%
54308504 1
< 0.1%
54308574 1
< 0.1%
54308595 1
< 0.1%
54308598 1
< 0.1%
54315725 1
< 0.1%
54316003 1
< 0.1%
ValueCountFrequency (%)
150525582 1
< 0.1%
150524077 1
< 0.1%
150520714 1
< 0.1%
150520444 1
< 0.1%
150519879 1
< 0.1%
150519354 1
< 0.1%
150508884 1
< 0.1%
150506690 1
< 0.1%
150505612 1
< 0.1%
150501875 1
< 0.1%

id_store
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4984.8651
Minimum360
Maximum9084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:21.897972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum360
5-th percentile1796
Q11796
median4542
Q37965
95-th percentile9084
Maximum9084
Range8724
Interquartile range (IQR)6169

Descriptive statistics

Standard deviation2575.8883
Coefficient of variation (CV)0.51674183
Kurtosis-1.1749142
Mean4984.8651
Median Absolute Deviation (MAD)2746
Skewness-0.0060064917
Sum5.8474462 × 108
Variance6635200.6
MonotonicityNot monotonic
2023-11-08T15:29:22.057530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1796 27349
23.3%
4337 20003
17.1%
4542 17641
15.0%
6293 16103
13.7%
8283 10023
 
8.5%
7965 9091
 
7.7%
9084 7804
 
6.7%
8052 4906
 
4.2%
360 4383
 
3.7%
8347 1
 
< 0.1%
ValueCountFrequency (%)
360 4383
 
3.7%
1796 27349
23.3%
4337 20003
17.1%
4542 17641
15.0%
6293 16103
13.7%
7965 9091
 
7.7%
8052 4906
 
4.2%
8283 10023
 
8.5%
8347 1
 
< 0.1%
9084 7804
 
6.7%
ValueCountFrequency (%)
9084 7804
 
6.7%
8347 1
 
< 0.1%
8283 10023
 
8.5%
8052 4906
 
4.2%
7965 9091
 
7.7%
6293 16103
13.7%
4542 17641
15.0%
4337 20003
17.1%
1796 27349
23.3%
360 4383
 
3.7%

id_table
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)0.4%
Missing95387
Missing (%)81.3%
Infinite0
Infinite (%)0.0%
Mean152108.64
Minimum3199
Maximum288083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:22.272538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3199
5-th percentile3202
Q1113481
median166328
Q3198340
95-th percentile265242
Maximum288083
Range284884
Interquartile range (IQR)84859

Descriptive statistics

Standard deviation75645.829
Coefficient of variation (CV)0.49731447
Kurtosis0.019984883
Mean152108.64
Median Absolute Deviation (MAD)32013
Skewness-0.89992451
Sum3.3337651 × 109
Variance5.7222915 × 109
MonotonicityNot monotonic
2023-11-08T15:29:22.502357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113481 689
 
0.6%
3199 621
 
0.5%
166322 613
 
0.5%
206103 598
 
0.5%
198339 594
 
0.5%
206096 576
 
0.5%
206102 536
 
0.5%
184593 500
 
0.4%
166314 499
 
0.4%
184589 451
 
0.4%
Other values (87) 16240
 
13.8%
(Missing) 95387
81.3%
ValueCountFrequency (%)
3199 621
0.5%
3200 278
0.2%
3201 165
 
0.1%
3202 153
 
0.1%
3203 133
 
0.1%
3204 334
0.3%
3205 398
0.3%
3206 351
0.3%
3207 406
0.3%
3208 189
 
0.2%
ValueCountFrequency (%)
288083 30
 
< 0.1%
288082 26
 
< 0.1%
288081 36
 
< 0.1%
288080 54
 
< 0.1%
288078 91
 
0.1%
288075 120
0.1%
288074 263
0.2%
288073 287
0.2%
265242 215
0.2%
244837 182
0.2%

id_waiter
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing59927
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean8236.9124
Minimum472
Maximum18911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:22.703540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum472
5-th percentile472
Q12418
median2418
Q316793
95-th percentile18911
Maximum18911
Range18439
Interquartile range (IQR)14375

Descriptive statistics

Standard deviation6996.0912
Coefficient of variation (CV)0.84935845
Kurtosis-1.5139972
Mean8236.9124
Median Absolute Deviation (MAD)1946
Skewness0.49543935
Sum4.7260932 × 108
Variance48945292
MonotonicityNot monotonic
2023-11-08T15:29:22.824980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2418 24667
21.0%
16793 10023
 
8.5%
7588 9152
 
7.8%
18911 7804
 
6.7%
472 4129
 
3.5%
15977 1087
 
0.9%
16199 507
 
0.4%
11585 8
 
< 0.1%
(Missing) 59927
51.1%
ValueCountFrequency (%)
472 4129
 
3.5%
2418 24667
21.0%
7588 9152
 
7.8%
11585 8
 
< 0.1%
15977 1087
 
0.9%
16199 507
 
0.4%
16793 10023
8.5%
18911 7804
 
6.7%
ValueCountFrequency (%)
18911 7804
 
6.7%
16793 10023
8.5%
16199 507
 
0.4%
15977 1087
 
0.9%
11585 8
 
< 0.1%
7588 9152
 
7.8%
2418 24667
21.0%
472 4129
 
3.5%

id_customer
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct833
Distinct (%)44.6%
Missing115437
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean1904322.7
Minimum1206843
Maximum2783146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:23.052610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1206843
5-th percentile1638954
Q11653653
median1843156
Q32126142
95-th percentile2355539
Maximum2783146
Range1576303
Interquartile range (IQR)472489

Descriptive statistics

Standard deviation277784.24
Coefficient of variation (CV)0.14587036
Kurtosis-0.36494318
Mean1904322.7
Median Absolute Deviation (MAD)197470
Skewness0.46261792
Sum3.5553706 × 109
Variance7.7164085 × 1010
MonotonicityNot monotonic
2023-11-08T15:29:23.294442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1647303 46
 
< 0.1%
1646350 41
 
< 0.1%
1642601 39
 
< 0.1%
1644184 36
 
< 0.1%
1647065 34
 
< 0.1%
1647698 33
 
< 0.1%
1668408 31
 
< 0.1%
1652738 28
 
< 0.1%
1638954 27
 
< 0.1%
1654937 25
 
< 0.1%
Other values (823) 1527
 
1.3%
(Missing) 115437
98.4%
ValueCountFrequency (%)
1206843 1
 
< 0.1%
1220796 1
 
< 0.1%
1221186 2
< 0.1%
1221743 1
 
< 0.1%
1240617 1
 
< 0.1%
1247531 1
 
< 0.1%
1249747 1
 
< 0.1%
1250394 3
< 0.1%
1251295 1
 
< 0.1%
1251359 1
 
< 0.1%
ValueCountFrequency (%)
2783146 1
< 0.1%
2778917 1
< 0.1%
2765702 1
< 0.1%
2760654 1
< 0.1%
2757354 1
< 0.1%
2756847 1
< 0.1%
2737762 1
< 0.1%
2729843 1
< 0.1%
2722728 1
< 0.1%
2716470 2
< 0.1%

id_external
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct117185
Distinct (%)> 99.9%
Missing72
Missing (%)0.1%
Memory size916.6 KiB
b6f1b0aa-dbed-4130-8691-631d301445b0
 
3
a03e0170-8b58-48f4-aaf6-17e7b7f3ffe5
 
3
772162b4-5447-4be2-857e-173a039c6ffc
 
3
8e555d4c-fd01-45d5-96b9-31b96954c2a3
 
2
e6855374-1ea4-4cab-9e90-c1fdd36f56b9
 
2
Other values (117180)
117219 

Length

Max length36
Median length36
Mean length35.976747
Min length7

Characters and Unicode

Total characters4217626
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117141 ?
Unique (%)99.9%

Sample

1st row0425716B-EFF4-41CA-AEA1-839104F36833
2nd row75E41FE2-64FF-41D3-954C-A7DE4AA887EF
3rd rowF6051A05-C9AC-4033-BF72-BB5149B8F439
4th rowB8BEEC66-1C10-48A0-B4D5-035CB5EEFE62
5th row17F0533C-2FF1-4FC5-A50D-12704C7B7A4B

Common Values

ValueCountFrequency (%)
b6f1b0aa-dbed-4130-8691-631d301445b0 3
 
< 0.1%
a03e0170-8b58-48f4-aaf6-17e7b7f3ffe5 3
 
< 0.1%
772162b4-5447-4be2-857e-173a039c6ffc 3
 
< 0.1%
8e555d4c-fd01-45d5-96b9-31b96954c2a3 2
 
< 0.1%
e6855374-1ea4-4cab-9e90-c1fdd36f56b9 2
 
< 0.1%
a7de716f-6684-485e-b012-9a3914bf59a0 2
 
< 0.1%
81e09f6c-3c7d-4a62-89ad-76d91592688f 2
 
< 0.1%
ca97b0d1-b74e-426a-8347-9c9478c834ce 2
 
< 0.1%
374ad4c6-d8f6-4aa6-871f-4205af7f153e 2
 
< 0.1%
d0d99bed-ffc6-4fd5-a31b-eb14cb6ff5c5 2
 
< 0.1%
Other values (117175) 117209
99.9%
(Missing) 72
 
0.1%

Length

2023-11-08T15:29:23.531471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b6f1b0aa-dbed-4130-8691-631d301445b0 3
 
< 0.1%
772162b4-5447-4be2-857e-173a039c6ffc 3
 
< 0.1%
a03e0170-8b58-48f4-aaf6-17e7b7f3ffe5 3
 
< 0.1%
4d9acc25-3b88-43cf-b437-153e414d9a09 2
 
< 0.1%
7e875325-4e25-44c7-a3d1-89d65a3cf6bd 2
 
< 0.1%
20d595cf-9e7b-4308-af37-d38292ec2617 2
 
< 0.1%
02404df3-9617-4618-9ef0-5c52e64a4dcb 2
 
< 0.1%
aeb3a3f6-02e3-4061-aa4b-bc37ea187980 2
 
< 0.1%
fdc4cd2c-a0c1-496a-aadc-297e2a023e5f 2
 
< 0.1%
bd1662da-6fea-41ed-81b1-22919fc098b1 2
 
< 0.1%
Other values (117175) 117209
> 99.9%

Most occurring characters

ValueCountFrequency (%)
- 468552
 
11.1%
4 336376
 
8.0%
9 249462
 
5.9%
B 248814
 
5.9%
8 248531
 
5.9%
A 247423
 
5.9%
6 220732
 
5.2%
7 220173
 
5.2%
0 219970
 
5.2%
2 219747
 
5.2%
Other values (13) 1537846
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2373398
56.3%
Uppercase Letter 1371838
32.5%
Dash Punctuation 468552
 
11.1%
Lowercase Letter 3838
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 336376
14.2%
9 249462
10.5%
8 248531
10.5%
6 220732
9.3%
7 220173
9.3%
0 219970
9.3%
2 219747
9.3%
1 219610
9.3%
3 219531
9.2%
5 219266
9.2%
Uppercase Letter
ValueCountFrequency (%)
B 248814
18.1%
A 247423
18.0%
D 219558
16.0%
C 219178
16.0%
E 218555
15.9%
F 218310
15.9%
Lowercase Letter
ValueCountFrequency (%)
b 678
17.7%
a 669
17.4%
d 641
16.7%
c 632
16.5%
f 612
15.9%
e 606
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 468552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2841950
67.4%
Latin 1375676
32.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 248814
18.1%
A 247423
18.0%
D 219558
16.0%
C 219178
15.9%
E 218555
15.9%
F 218310
15.9%
b 678
 
< 0.1%
a 669
 
< 0.1%
d 641
 
< 0.1%
c 632
 
< 0.1%
Other values (2) 1218
 
0.1%
Common
ValueCountFrequency (%)
- 468552
16.5%
4 336376
11.8%
9 249462
8.8%
8 248531
8.7%
6 220732
7.8%
7 220173
7.7%
0 219970
7.7%
2 219747
7.7%
1 219610
7.7%
3 219531
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4217626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 468552
 
11.1%
4 336376
 
8.0%
9 249462
 
5.9%
B 248814
 
5.9%
8 248531
 
5.9%
A 247423
 
5.9%
6 220732
 
5.2%
7 220173
 
5.2%
0 219970
 
5.2%
2 219747
 
5.2%
Other values (13) 1537846
36.5%

id_device
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)< 0.1%
Missing8398
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean11238.598
Minimum129
Maximum17755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:23.708351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile6694
Q17411
median10820
Q315733
95-th percentile17755
Maximum17755
Range17626
Interquartile range (IQR)8322

Descriptive statistics

Standard deviation4577.8719
Coefficient of variation (CV)0.4073348
Kurtosis-0.67123007
Mean11238.598
Median Absolute Deviation (MAD)4126
Skewness-0.19810361
Sum1.2239507 × 109
Variance20956911
MonotonicityNot monotonic
2023-11-08T15:29:23.826305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6694 19838
16.9%
7411 17641
15.0%
11882 16103
13.7%
10820 14284
12.2%
17755 9139
7.8%
15161 9091
7.7%
15733 5267
 
4.5%
17320 5173
 
4.4%
16813 4733
 
4.0%
129 4057
 
3.5%
Other values (3) 3580
 
3.1%
(Missing) 8398
7.2%
ValueCountFrequency (%)
129 4057
 
3.5%
6694 19838
16.9%
7411 17641
15.0%
10820 14284
12.2%
11882 16103
13.7%
15161 9091
7.7%
15327 172
 
0.1%
15733 5267
 
4.5%
15894 1
 
< 0.1%
16813 4733
 
4.0%
ValueCountFrequency (%)
17755 9139
7.8%
17383 3407
 
2.9%
17320 5173
 
4.4%
16813 4733
 
4.0%
15894 1
 
< 0.1%
15733 5267
 
4.5%
15327 172
 
0.1%
15161 9091
7.7%
11882 16103
13.7%
10820 14284
12.2%
Distinct116676
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size916.6 KiB
Minimum2019-01-01 01:23:18+00:00
Maximum2020-11-18 18:53:33+00:00
2023-11-08T15:29:24.677016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:24.865417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct116751
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size916.6 KiB
Minimum2019-01-01 01:23:25+00:00
Maximum2020-11-18 18:54:42+00:00
2023-11-08T15:29:25.102773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:25.328617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dim_name
Categorical

HIGH CARDINALITY  MISSING 

Distinct1799
Distinct (%)3.2%
Missing60751
Missing (%)51.8%
Memory size916.6 KiB
A1
 
740
A2
 
724
A3
 
712
A4
 
695
A5
 
669
Other values (1794)
53013 

Length

Max length31
Median length3
Mean length3.0695277
Min length1

Characters and Unicode

Total characters173591
Distinct characters87
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique977 ?
Unique (%)1.7%

Sample

1st rowvincent
2nd rowfrere et soeur avec pierre
3rd rowrachel
4th rowGroupe PEL
5th rowremi et date

Common Values

ValueCountFrequency (%)
A1 740
 
0.6%
A2 724
 
0.6%
A3 712
 
0.6%
A4 695
 
0.6%
A5 669
 
0.6%
A6 661
 
0.6%
A7 649
 
0.6%
A8 641
 
0.5%
A9 625
 
0.5%
A10 618
 
0.5%
Other values (1789) 49819
42.5%
(Missing) 60751
51.8%

Length

2023-11-08T15:29:25.580645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a1 741
 
1.3%
a2 724
 
1.2%
a3 712
 
1.2%
a4 696
 
1.2%
a5 669
 
1.1%
a6 661
 
1.1%
a7 649
 
1.1%
a8 641
 
1.1%
a9 625
 
1.1%
a10 618
 
1.1%
Other values (1699) 51765
88.5%

Most occurring characters

ValueCountFrequency (%)
A 20477
 
11.8%
1 19178
 
11.0%
2 14592
 
8.4%
K 12119
 
7.0%
3 11359
 
6.5%
4 9857
 
5.7%
N 9147
 
5.3%
5 8767
 
5.1%
B 8255
 
4.8%
6 7658
 
4.4%
Other values (77) 52182
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94166
54.2%
Uppercase Letter 54528
31.4%
Lowercase Letter 21781
 
12.5%
Space Separator 2084
 
1.2%
Other Punctuation 453
 
0.3%
Dash Punctuation 310
 
0.2%
Open Punctuation 106
 
0.1%
Close Punctuation 105
 
0.1%
Math Symbol 39
 
< 0.1%
Other Symbol 9
 
< 0.1%
Other values (3) 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2989
13.7%
a 2813
12.9%
r 2133
9.8%
t 1748
 
8.0%
i 1445
 
6.6%
o 1292
 
5.9%
l 1285
 
5.9%
u 1206
 
5.5%
c 1161
 
5.3%
n 1050
 
4.8%
Other values (21) 4659
21.4%
Uppercase Letter
ValueCountFrequency (%)
A 20477
37.6%
K 12119
22.2%
N 9147
16.8%
B 8255
15.1%
L 3754
 
6.9%
F 329
 
0.6%
M 119
 
0.2%
D 104
 
0.2%
E 36
 
0.1%
I 23
 
< 0.1%
Other values (16) 165
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 19178
20.4%
2 14592
15.5%
3 11359
12.1%
4 9857
10.5%
5 8767
9.3%
6 7658
 
8.1%
7 6521
 
6.9%
8 5817
 
6.2%
9 5242
 
5.6%
0 5175
 
5.5%
Other Punctuation
ValueCountFrequency (%)
% 309
68.2%
: 94
 
20.8%
? 29
 
6.4%
, 10
 
2.2%
/ 6
 
1.3%
& 4
 
0.9%
. 1
 
0.2%
Other Symbol
ValueCountFrequency (%)
5
55.6%
1
 
11.1%
1
 
11.1%
1
 
11.1%
1
 
11.1%
Space Separator
ValueCountFrequency (%)
2084
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 310
100.0%
Open Punctuation
ValueCountFrequency (%)
( 106
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Math Symbol
ValueCountFrequency (%)
+ 39
100.0%
Nonspacing Mark
ValueCountFrequency (%)
7
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97273
56.0%
Latin 76309
44.0%
Inherited 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 20477
26.8%
K 12119
15.9%
N 9147
12.0%
B 8255
10.8%
L 3754
 
4.9%
e 2989
 
3.9%
a 2813
 
3.7%
r 2133
 
2.8%
t 1748
 
2.3%
i 1445
 
1.9%
Other values (47) 11429
15.0%
Common
ValueCountFrequency (%)
1 19178
19.7%
2 14592
15.0%
3 11359
11.7%
4 9857
10.1%
5 8767
9.0%
6 7658
 
7.9%
7 6521
 
6.7%
8 5817
 
6.0%
9 5242
 
5.4%
0 5175
 
5.3%
Other values (18) 3107
 
3.2%
Inherited
ValueCountFrequency (%)
7
77.8%
2
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173547
> 99.9%
None 25
 
< 0.1%
VS 7
 
< 0.1%
Dingbats 5
 
< 0.1%
Punctuation 3
 
< 0.1%
Misc Symbols 3
 
< 0.1%
Specials 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 20477
 
11.8%
1 19178
 
11.1%
2 14592
 
8.4%
K 12119
 
7.0%
3 11359
 
6.5%
4 9857
 
5.7%
N 9147
 
5.3%
5 8767
 
5.1%
B 8255
 
4.8%
6 7658
 
4.4%
Other values (64) 52138
30.0%
None
ValueCountFrequency (%)
é 20
80.0%
î 2
 
8.0%
ï 1
 
4.0%
ë 1
 
4.0%
à 1
 
4.0%
VS
ValueCountFrequency (%)
7
100.0%
Dingbats
ValueCountFrequency (%)
5
100.0%
Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Specials
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

dim_status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size916.6 KiB
CLOSED
116939 
IN_PROGRESS
 
365

Length

Max length11
Median length6
Mean length6.0155579
Min length6

Characters and Unicode

Total characters705649
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLOSED
2nd rowCLOSED
3rd rowCLOSED
4th rowCLOSED
5th rowCLOSED

Common Values

ValueCountFrequency (%)
CLOSED 116939
99.7%
IN_PROGRESS 365
 
0.3%

Length

2023-11-08T15:29:25.780858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T15:29:25.926754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
closed 116939
99.7%
in_progress 365
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 117669
16.7%
O 117304
16.6%
E 117304
16.6%
C 116939
16.6%
L 116939
16.6%
D 116939
16.6%
R 730
 
0.1%
I 365
 
0.1%
N 365
 
0.1%
_ 365
 
0.1%
Other values (2) 730
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 705284
99.9%
Connector Punctuation 365
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 117669
16.7%
O 117304
16.6%
E 117304
16.6%
C 116939
16.6%
L 116939
16.6%
D 116939
16.6%
R 730
 
0.1%
I 365
 
0.1%
N 365
 
0.1%
P 365
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 705284
99.9%
Common 365
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 117669
16.7%
O 117304
16.6%
E 117304
16.6%
C 116939
16.6%
L 116939
16.6%
D 116939
16.6%
R 730
 
0.1%
I 365
 
0.1%
N 365
 
0.1%
P 365
 
0.1%
Common
ValueCountFrequency (%)
_ 365
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 705649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 117669
16.7%
O 117304
16.6%
E 117304
16.6%
C 116939
16.6%
L 116939
16.6%
D 116939
16.6%
R 730
 
0.1%
I 365
 
0.1%
N 365
 
0.1%
_ 365
 
0.1%
Other values (2) 730
 
0.1%

dim_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size916.6 KiB
1
117301 
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters117304
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

Length

2023-11-08T15:29:26.043951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T15:29:26.169755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117304
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 117304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 117301
> 99.9%
3 3
 
< 0.1%

dim_comment
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing117210
Missing (%)99.9%
Memory size916.6 KiB
Delivery
94 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters752
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelivery
2nd rowDelivery
3rd rowDelivery
4th rowDelivery
5th rowDelivery

Common Values

ValueCountFrequency (%)
Delivery 94
 
0.1%
(Missing) 117210
99.9%

Length

2023-11-08T15:29:26.259442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T15:29:26.398232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
delivery 94
100.0%

Most occurring characters

ValueCountFrequency (%)
e 188
25.0%
D 94
12.5%
l 94
12.5%
i 94
12.5%
v 94
12.5%
r 94
12.5%
y 94
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 658
87.5%
Uppercase Letter 94
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 188
28.6%
l 94
14.3%
i 94
14.3%
v 94
14.3%
r 94
14.3%
y 94
14.3%
Uppercase Letter
ValueCountFrequency (%)
D 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 188
25.0%
D 94
12.5%
l 94
12.5%
i 94
12.5%
v 94
12.5%
r 94
12.5%
y 94
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 188
25.0%
D 94
12.5%
l 94
12.5%
i 94
12.5%
v 94
12.5%
r 94
12.5%
y 94
12.5%

dim_source
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size916.6 KiB
Tiller iPAD
62889 
tiller-order
53923 
LAFOURCHETTE
 
326
other
 
166

Length

Max length12
Median length11
Mean length11.453974
Min length5

Characters and Unicode

Total characters1343597
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTiller iPAD
2nd rowTiller iPAD
3rd rowTiller iPAD
4th rowtiller-order
5th rowTiller iPAD

Common Values

ValueCountFrequency (%)
Tiller iPAD 62889
53.6%
tiller-order 53923
46.0%
LAFOURCHETTE 326
 
0.3%
other 166
 
0.1%

Length

2023-11-08T15:29:26.551519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-08T15:29:26.773125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
tiller 62889
34.9%
ipad 62889
34.9%
tiller-order 53923
29.9%
lafourchette 326
 
0.2%
other 166
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 233624
17.4%
r 224824
16.7%
i 179701
13.4%
e 170901
12.7%
T 63541
 
4.7%
A 63215
 
4.7%
62889
 
4.7%
P 62889
 
4.7%
D 62889
 
4.7%
o 54089
 
4.0%
Other values (12) 165035
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 971317
72.3%
Uppercase Letter 255468
 
19.0%
Space Separator 62889
 
4.7%
Dash Punctuation 53923
 
4.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 63541
24.9%
A 63215
24.7%
P 62889
24.6%
D 62889
24.6%
E 652
 
0.3%
L 326
 
0.1%
F 326
 
0.1%
O 326
 
0.1%
U 326
 
0.1%
R 326
 
0.1%
Other values (2) 652
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 233624
24.1%
r 224824
23.1%
i 179701
18.5%
e 170901
17.6%
o 54089
 
5.6%
t 54089
 
5.6%
d 53923
 
5.6%
h 166
 
< 0.1%
Space Separator
ValueCountFrequency (%)
62889
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53923
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1226785
91.3%
Common 116812
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 233624
19.0%
r 224824
18.3%
i 179701
14.6%
e 170901
13.9%
T 63541
 
5.2%
A 63215
 
5.2%
P 62889
 
5.1%
D 62889
 
5.1%
o 54089
 
4.4%
t 54089
 
4.4%
Other values (10) 57023
 
4.6%
Common
ValueCountFrequency (%)
62889
53.8%
- 53923
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1343597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 233624
17.4%
r 224824
16.7%
i 179701
13.4%
e 170901
12.7%
T 63541
 
4.7%
A 63215
 
4.7%
62889
 
4.7%
P 62889
 
4.7%
D 62889
 
4.7%
o 54089
 
4.0%
Other values (12) 165035
12.3%

m_nb_customer
Real number (ℝ)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3795693
Minimum0
Maximum62
Zeros356
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:26.953588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1540088
Coefficient of variation (CV)0.83649935
Kurtosis311.56946
Mean1.3795693
Median Absolute Deviation (MAD)0
Skewness11.000394
Sum161829
Variance1.3317364
MonotonicityNot monotonic
2023-11-08T15:29:27.161422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 92439
78.8%
2 16176
 
13.8%
3 3273
 
2.8%
4 2565
 
2.2%
5 1071
 
0.9%
6 622
 
0.5%
0 356
 
0.3%
7 275
 
0.2%
8 192
 
0.2%
9 108
 
0.1%
Other values (30) 227
 
0.2%
ValueCountFrequency (%)
0 356
 
0.3%
1 92439
78.8%
2 16176
 
13.8%
3 3273
 
2.8%
4 2565
 
2.2%
5 1071
 
0.9%
6 622
 
0.5%
7 275
 
0.2%
8 192
 
0.2%
9 108
 
0.1%
ValueCountFrequency (%)
62 1
< 0.1%
60 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
42 1
< 0.1%
40 2
< 0.1%
36 1
< 0.1%
35 2
< 0.1%
32 2
< 0.1%

m_cached_payed
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3969
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.294191
Minimum0
Maximum11512.5
Zeros732
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:27.393655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111.8
median14.1
Q330
95-th percentile117
Maximum11512.5
Range11512.5
Interquartile range (IQR)18.2

Descriptive statistics

Standard deviation111.24697
Coefficient of variation (CV)2.9050612
Kurtosis1176.2049
Mean38.294191
Median Absolute Deviation (MAD)4.6
Skewness20.408436
Sum4492061.8
Variance12375.888
MonotonicityNot monotonic
2023-11-08T15:29:27.638829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 12950
 
11.0%
12.9 5319
 
4.5%
16 3391
 
2.9%
9.5 3357
 
2.9%
9 3053
 
2.6%
9.9 3022
 
2.6%
11 2397
 
2.0%
13.5 2117
 
1.8%
12 1926
 
1.6%
18 1773
 
1.5%
Other values (3959) 77999
66.5%
ValueCountFrequency (%)
0 732
0.6%
0.02 4
 
< 0.1%
0.14 1
 
< 0.1%
0.2 1
 
< 0.1%
0.35 1
 
< 0.1%
0.4 4
 
< 0.1%
0.45 1
 
< 0.1%
0.5 41
 
< 0.1%
0.56 1
 
< 0.1%
0.6 1
 
< 0.1%
ValueCountFrequency (%)
11512.5 1
< 0.1%
5504.2 1
< 0.1%
3631.5 1
< 0.1%
3550.5 1
< 0.1%
3017.5 1
< 0.1%
3000 1
< 0.1%
2895.5 1
< 0.1%
2830 1
< 0.1%
2778 1
< 0.1%
2740 1
< 0.1%

m_cached_price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3968
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.298951
Minimum0
Maximum11512.5
Zeros699
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size916.6 KiB
2023-11-08T15:29:27.821261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111.8
median14.1
Q330
95-th percentile117
Maximum11512.5
Range11512.5
Interquartile range (IQR)18.2

Descriptive statistics

Standard deviation111.24241
Coefficient of variation (CV)2.9045811
Kurtosis1176.3944
Mean38.298951
Median Absolute Deviation (MAD)4.6
Skewness20.410679
Sum4492620.2
Variance12374.874
MonotonicityNot monotonic
2023-11-08T15:29:28.018520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 12952
 
11.0%
12.9 5319
 
4.5%
16 3392
 
2.9%
9.5 3357
 
2.9%
9 3053
 
2.6%
9.9 3022
 
2.6%
11 2397
 
2.0%
13.5 2117
 
1.8%
12 1926
 
1.6%
18 1773
 
1.5%
Other values (3958) 77996
66.5%
ValueCountFrequency (%)
0 699
0.6%
0.02 4
 
< 0.1%
0.14 1
 
< 0.1%
0.2 1
 
< 0.1%
0.35 1
 
< 0.1%
0.4 4
 
< 0.1%
0.45 1
 
< 0.1%
0.5 41
 
< 0.1%
0.56 1
 
< 0.1%
0.6 1
 
< 0.1%
ValueCountFrequency (%)
11512.5 1
< 0.1%
5504.2 1
< 0.1%
3631.5 1
< 0.1%
3550.5 1
< 0.1%
3017.5 1
< 0.1%
3000 1
< 0.1%
2895.5 1
< 0.1%
2830 1
< 0.1%
2778 1
< 0.1%
2740 1
< 0.1%

Interactions

2023-11-08T15:29:17.625890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:04.196551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:05.896494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.653833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.155785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:10.933789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:12.651250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:14.123282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:15.874569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:17.836972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:04.380921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:06.100697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.791138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.352137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:11.138429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:12.850096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:14.324955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:16.086205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:18.023677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:04.568514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:06.280081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.917753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.534961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:11.287454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.015594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:14.463217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:16.275125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:18.237606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:04.727802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:06.410876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:08.115271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.756846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:11.389967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.162390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:14.632846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:16.499224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:18.451436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:04.854795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:06.577586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:08.326983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.963480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:11.607017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.326009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:14.785171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:16.715483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:18.680782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:05.074846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:06.808282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:08.483402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:10.125737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:11.826421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.484347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:15.020982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:16.939218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:18.886819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:05.313646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.036843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:08.686390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:10.365848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:12.034237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.643478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:15.251774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:17.104014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:19.095035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:05.543213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.251969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:08.830426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:10.601224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:12.233004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.805467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:15.470033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:17.249398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:19.297791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:05.756860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:07.461327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:09.021074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:10.763805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:12.447364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:13.970135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:15.697671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-08T15:29:17.424947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-11-08T15:29:28.233222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
id_orderid_storeid_tableid_waiterid_customerid_devicem_nb_customerm_cached_payedm_cached_pricedim_statusdim_typedim_source
id_order1.0000.2610.2570.3840.4220.2150.1180.1080.1080.0480.0150.304
id_store0.2611.0000.9551.0000.1040.3410.3200.4230.4230.1990.0120.480
id_table0.2570.9551.0000.8970.6350.9570.080-0.031-0.0310.0200.0000.483
id_waiter0.3841.0000.8971.0000.0010.1060.1280.3520.3530.0841.0000.655
id_customer0.4220.1040.6350.0011.0000.260-0.0100.0570.0570.4010.2670.426
id_device0.2150.3410.9570.1060.2601.0000.2000.0670.0670.0160.0050.530
m_nb_customer0.1180.3200.0800.128-0.0100.2001.0000.5000.4990.0130.0000.028
m_cached_payed0.1080.423-0.0310.3520.0570.0670.5001.0000.9990.0000.0000.022
m_cached_price0.1080.423-0.0310.3530.0570.0670.4990.9991.0000.0000.0000.022
dim_status0.0480.1990.0200.0840.4010.0160.0130.0000.0001.0000.0000.811
dim_type0.0150.0120.0001.0000.2670.0050.0000.0000.0000.0001.0000.044
dim_source0.3040.4800.4830.6550.4260.5300.0280.0220.0220.8110.0441.000

Missing values

2023-11-08T15:29:19.678243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-08T15:29:20.346705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-08T15:29:21.042407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_orderid_storeid_tableid_waiterid_customerid_externalid_devicedate_openeddate_closeddim_namedim_statusdim_typedim_commentdim_sourcem_nb_customerm_cached_payedm_cached_price
0555388678052NaNNaNNaN0425716B-EFF4-41CA-AEA1-839104F3683315327.02019-01-12 13:02:17+00:002019-01-12 19:58:38+00:00vincentCLOSED1NaNTiller iPAD145.545.5
1560353098052NaNNaNNaN75E41FE2-64FF-41D3-954C-A7DE4AA887EF15327.02019-01-16 19:39:09+00:002019-01-16 22:10:50+00:00frere et soeur avec pierreCLOSED1NaNTiller iPAD249.849.8
2555500518052NaNNaNNaNF6051A05-C9AC-4033-BF72-BB5149B8F43915327.02019-01-12 14:18:46+00:002019-01-12 19:50:32+00:00rachelCLOSED1NaNTiller iPAD127.427.4
3570001198052NaN16199.0NaNB8BEEC66-1C10-48A0-B4D5-035CB5EEFE6215327.02019-01-24 17:49:12+00:002019-01-24 21:58:59+00:00Groupe PELCLOSED1NaNtiller-order360.060.0
4555588178052NaNNaNNaN17F0533C-2FF1-4FC5-A50D-12704C7B7A4B15327.02019-01-12 15:25:06+00:002019-01-12 19:21:03+00:00remi et dateCLOSED1NaNTiller iPAD239.939.9
5554514778052NaNNaNNaN570F71DE-504D-4040-BE10-19B5FDF64FAD15327.02019-01-11 19:13:15+00:002019-01-11 23:22:04+00:00abdelCLOSED1NaNTiller iPAD125.025.0
6560305638052NaNNaNNaN0BEB2A9B-4A3A-4BEE-AC13-8BDCB03DF56815327.02019-01-16 19:04:08+00:002019-01-16 22:13:55+00:00pierre + 6CLOSED1NaNTiller iPAD7220.7220.7
7563100868052NaNNaNNaN5522AFFD-BC4A-4F24-8129-14400BC529AA15327.02019-01-18 19:06:17+00:002019-01-18 23:13:08+00:00oceane +3CLOSED1NaNTiller iPAD4111.0111.0
8543266998052NaNNaNNaNE41B347F-840F-4067-90F4-76D3E5258A2B15327.02019-01-01 16:33:33+00:002019-01-01 23:47:28+00:00margaux, agathe et melanieCLOSED1NaNTiller iPAD341.041.0
9564795268052NaNNaNNaNB47CEA06-6AD3-406D-81F1-92DA5650026715327.02019-01-19 21:44:00+00:002019-01-19 23:15:16+00:00filles canapéCLOSED1NaNTiller iPAD333.933.9
id_orderid_storeid_tableid_waiterid_customerid_externalid_devicedate_openeddate_closeddim_namedim_statusdim_typedim_commentdim_sourcem_nb_customerm_cached_payedm_cached_price
1172941276440324542NaN7588.0NaND85D8798-294A-4E7C-8E2A-EB04E11D3C457411.02020-06-11 10:24:51+00:002020-06-11 10:25:37+00:00A8CLOSED1NaNtiller-order132.032.0
1172951074065124542NaN7588.0NaND921B715-5611-451B-BF95-31797FEB05DC7411.02019-12-09 11:17:58+00:002019-12-09 11:18:24+00:00A8CLOSED1NaNtiller-order129.029.0
1172961091085344542NaN7588.0NaN03ED1712-0D8F-4282-8504-A01964E5A0927411.02019-12-19 11:34:27+00:002019-12-19 11:36:12+00:00A11CLOSED1NaNtiller-order149.049.0
1172971277887834542NaN7588.0NaN29291CDC-7D3E-48BE-AFD0-269ADAFFCFFD7411.02020-06-12 10:47:26+00:002020-06-12 10:47:59+00:00A15CLOSED1NaNtiller-order125.025.0
1172981116774854542NaN7588.0NaN5361A0BE-AAD3-4339-8DAE-7F2BB46E0D677411.02020-01-07 12:01:49+00:002020-01-07 12:03:50+00:00A37CLOSED1NaNtiller-order126.026.0
117299616304714542NaNNaNNaNC4A0AFD8-FBD7-4740-A48B-4890F46E43E27411.02019-02-28 13:13:21+00:002019-02-28 13:38:45+00:00NaNCLOSED1NaNTiller iPAD123.023.0
117300767727304542NaNNaNNaN95A17B0B-22AE-43A1-8BEB-B105BF3376557411.02019-06-11 11:08:01+00:002019-06-11 11:10:26+00:00NaNCLOSED1NaNTiller iPAD126.026.0
117301777992274542NaNNaNNaN0E5D4843-7EA3-4D9E-9490-EB9717A0220F7411.02019-06-17 10:30:50+00:002019-06-17 10:32:45+00:00NaNCLOSED1NaNTiller iPAD129.029.0
117302606271604542NaNNaNNaNCE760A3B-4464-462A-AA2C-CCFEB88EEC397411.02019-02-21 11:50:58+00:002019-02-21 11:51:56+00:00NaNCLOSED1NaNTiller iPAD132.032.0
117303932238214542NaNNaNNaNF004A307-C7C9-4508-A587-49ED5CF8F1567411.02019-09-18 11:32:35+00:002019-09-18 11:39:38+00:00NaNCLOSED1NaNTiller iPAD117.017.0